The purpose of all scientific technologies is to serve the human being. The human being is always the focus of the scientific studies. In the computerized vision technology, the study of the human face is one popular topic. As one may see, most digital cameras, smartphones, etc. are equipped with the face detection, expression identification etc. functions. These applications, however, apply to the human face in general, not to the face of any particular person.
Face recognition is one of the most desirable biometric technologies. Face recognition addresses the identification of particular persons, not the human face in general. To identify whether an image contains human faces, common features such as two eyes and one nose would suffice. To identify whether an image contains the face of a particular person, a computer system needs to have a face image database. This system then needs to obtain the representative information of the candidates to be identified, before it can calculate and determine the identity of the input face image.
To accomplish the above goal, the designed computer system needs to extract discriminating features from face images of particular persons. As a result, the collection of useful data and the abstraction of the distinctive features are the most important tasks in the face recognition technology.
In the past, many researchers have proposed a variety of approaches to achieve these tasks. However, all these approaches are designed under the presumption that the images used for training are clean images without occlusion and disguise. In other words, these training face images may not be occluded by scarf, sunglasses or masks. Such presumption is not practical, since real-world face images collected and used in establishing the face image database are always not ideally captured. For example, the images may be corrupted by poor illumination conditions, blocked by occlusion or distorted by different view angles.
Therefore, it is necessary to provide an automated face image recognition method that automatically establishes useful face template images from images of poor quality, for used in recognition.
It is also necessary to provide an automated face image recognition method that correctly identifies human faces based on images with insufficient features.
It is also necessary to provide an automated image recognition method that automatically establishes useful template images from images of poor quality, for used in recognition.
It is also necessary to provide an automated image recognition method that correctly recognizes images with insufficient features.
It is also necessary to provide an automated image recognition system that implements these methods.